Struct ChatGroq 

pub struct ChatGroq { /* private fields */ }

Groq Chat Model

Fast inference using Groq’s LPU.

Example

use agent_io::llm::ChatGroq;

let llm = ChatGroq::new("llama-3.3-70b-versatile")?;

Implementations

impl ChatGroq

pub fn new(model: impl Into<String>) -> Result<Self, LlmError>

Create a new Groq chat model

pub fn builder() -> ChatGroqBuilder

Create a builder for configuration

Trait Implementations

impl BaseChatModel for ChatGroq

fn model(&self) -> &str

Get the model name

fn provider(&self) -> &str

Get the provider name

fn context_window(&self) -> Option<u64>

Get the context window size (max input tokens)

fn invoke<'life0, 'async_trait>( &'life0 self, messages: Vec<Message>, tools: Option<Vec<ToolDefinition>>, tool_choice: Option<ToolChoice>, ) -> Pin<Box<dyn Future<Output = Result<ChatCompletion, LlmError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Invoke the model with messages

fn invoke_stream<'life0, 'async_trait>( &'life0 self, messages: Vec<Message>, tools: Option<Vec<ToolDefinition>>, tool_choice: Option<ToolChoice>, ) -> Pin<Box<dyn Future<Output = Result<ChatStream, LlmError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Invoke the model with streaming response

fn count_tokens<'life0, 'life1, 'async_trait>( &'life0 self, messages: &'life1 [Message], ) -> Pin<Box<dyn Future<Output = u64> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Count tokens in messages (approximate)

fn supports_tools(&self) -> bool

Check if the model supports tools

fn supports_streaming(&self) -> bool

Check if the model supports streaming

fn supports_vision(&self) -> bool

Check if the model supports vision